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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Wang, Kaixiang Shang, Wenqian Commun Univ China Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781509055074

Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.

关键词： Naive Bayes classification algorithm DOTA2

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A Critical Study of classification algorithms Using Diabetes Diagnosis

A Critical Study of Classification Algorithms Using Diabetes...

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IEEE International Advance Computing Conference

作者： Panigrahi Srikanth Dharmaiah Deverapalli Department of Information Technology VNR VJIET Department of Information Technology Shri Vishnu Engineering College for Woman

ISBN: (纸本)9781467382878

This paper predicts the Diabetes Disease based on Data Mining Techniques of classification algorithms. classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is classification. classification algorithm Examine of the Decision Tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set classification algorithm Decision Tree algorithm Byes algorithms and Rules

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Change detection for activity recognition.

Change detection for activity recognition.

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作者： BASHIR, S. A. Robert Gordon University

学位级别：博士

Activity Recognition is concerned with identifying the physical state of a user at a par- ticular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user characteristics. However, the existing approaches to model adaptation in activity recognition are blind. They con- tinuously adapt a classification model at a regular interval without specific and precise detection of the indicator of the degrading performance of the model. This approach can lead to wastage of system resources dedicated to continuous adaptation. This thesis addresses the problem of detecting changes in the accuracy of activity recog- nition model. The thesis developed a classifier for activity recognition. The classifier uses three statistical summaries data that can be generated from any dataset for sim- ilarity based classification of new samples. The weighted ensemble combination of the classification decision from each statistical summary data results in a better performance than three existing benchmarked classification algorithms. The thesis also presents change detection approaches that can detect the changes in the accuracy of the underlying recognition model without having access to the ground truth label of each activity being recognised. The first approach called 'UDetect' computes the change statistics from the window of classified data and employed statistical process control method to detect variations between the classified data and the reference data of a class. Evaluation of the approach indicates a consistent detection that correlates with the error rate of the model. The second approach is a distance based change detection technique that r

关键词： Activity Recognition Change Detection classification algorithm Sensors

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FEATURE SELECTION IN MEDICAL DOMAINS

FEATURE SELECTION IN MEDICAL DOMAINS

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作者： SARA VAHID University of Georgia

学位级别：硕士

Usually datasets in the medical domain contain so many features while not all these features are useful for training the classifier. This research aims to find the best optimal subset of features for each medical datasets namely Heart Disease, Parkinson, Thoracic Surgery, Diabetic Retinopathy and Breast Cancer which are all publicly available in UCI repository. We use Filter and Wrapper as feature selection methods which are scheme independent and scheme specific, respectively. In Wrapper approach, we applied different classification algorithms such as Support Vector Machine (SVM), Naive Bayes, Decision Tree, Random Forest and K Nearest Neighbor (KNN) for both feature selection and classification. Afterwards, the same classifiers were applied on the dataset with selected features. The target of this study is comparative analysis of the effect of feature selection on 5 aforementioned datasets (these datasets are various in terms of data types and dimensionality) regarding the accuracy of the classifier.

关键词： Feature Selection Machine Learning classification algorithm Medical Domain

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An empirical study of classification algorithm evaluation for financial risk prediction

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APPLIED SOFT COMPUTING 2011年第2期11卷 2906-2915页

作者： Peng, Yi Wang, Guoxun Kou, Gang Shi, Yong Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China CAS Res Ctr Fictitious Econ & Data Sci Beijing 100080 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers' performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection. (C) 2010 Elsevier B.V. All rights reserved.

关键词： classification algorithm Multiple criteria decision making (MCDM) Financial risk prediction Knowledge-rich financial risk analysis

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Automatic classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2016年第4期63卷 850-858页

作者： Ranjbaran, Mina Smith, Heather L. H. Galiana, Henrietta L. McGill Univ Dept Biomed Engn 3775 Univ Montreal PQ H3A 2B4 Canada

The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity.

关键词： classification algorithm data clustering nystagmus system identification vestibulo-ocular reflex

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Study on Vegetation classification Based on Spectral Knowledge Base

Study on Vegetation Classification Based on Spectral Knowled...

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The International Conference on Intelligent Agriculture 2017(ICIA2017)

作者： Peng Liu Jingcheng Zhang Bin Wang Xuexue Zhang Kaihua Wu College of Life Information Science and Instrument Engineering Hangzhou Dianzi University

ISBN: (纸本)9783030061784

A framework about spectral based vegetation classification was proposed, which serves as a core methodology of the vegetation spectral knowledge base. The hyperspectral reflectances of 13 types of plants were measured by an ASD FieldSpec 4 spectroradiometer. Two forms of spectral features were used for representing the key spectral characteristics of plants,including Vegetation index(VI) and spectral shape features. Based on these spectral features, a sensitivity analysis was performed to identify the most important features for establishing the classifier. The analysis of variance(ANOVA) and the cross-correlation analysis were applied to derive the sensitivity of features and remove features that have high correlations. Then, a classification method for differentiating plants was established by coupling some spectral similarity measures(e.g., ED) with some classification methods(e.g.,BPANN and SVM). The results of discrimination analysis showed that a highest accuracy was produced by SVM with the OAA over 99% when using 7 sensitive VIs. The results suggested the framework about spectral based vegetation classification can form a basis for spectral knowledge base and application technology and further achieve a wide range of plant classification based on remote sensing.

关键词： Vegetation classification Hyperspectral Feature extraction classification algorithm

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On multiple-domain cooperative diversity for communications with distributed content in *** networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2017年第1期30卷

作者： Gacanin, Haris Ligata, Amir Chahibi, Youssef Alcatel Lucent Bell Mot Div Antwerp Belgium IPSA Inst Sarajevo Bosnia & Herceg Georgia Inst Technol Atlanta GA 30332 USA

Broadband data applications can be delivered to homes over the available home network infrastructure (i.e., mediums). Further enhancement of the network performance is possible by exploiting spatial, time, or frequency diversity. Recently, MIMO systems have been proposed for power line communication (PLC) networks. Because of the extremely diverse physical characteristics of the mediums, an implementation of MIMO systems in *** across different domains represents a non-trivial task. In this paper, we present and theoretically evaluate the performance of multiple-domain diversity mechanism in *** using a cross-layer classification algorithm. In the proposed network architecture, the signal is transmitted over different mediums selected by the classification algorithm in order to meet the quality-of-service demands. At the receiver, joint signal combining and equalization are done to take advantage of the multiple-domain cooperative diversity and increase the signal-to-noise ratio. The merit of the proposed multiple-domain PLC network has been confirmed by analytical performance analysis and computer simulation. Copyright (C) 2017 John Wiley & Sons, Ltd.

关键词： G.hn multiple-domain OFDM classification algorithm

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Design, Construction and Analysis of Model Dataset for Indian Road Network and Performing classification to Estimate Accuracy of Different Classifier with Its Comparison Summary Evaluation 1

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6th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Gothane, Suwarna Sarode, M. V. Raju, K. Srujan CMR Tech Campus Hyderabad Andhra Prades India Jagadambha Coll Engn & Technol Yavatmal India

ISBN: (数字)9783319489599

ISBN: (纸本)9783319489599;9783319489582

Road network consist of various problems. Pothole, crack and patches are the common problems of road network. Various manual and automated solutions have been proposed by the expertise in the previous work. To overcome the problem we have came here with a novel solution approach to identify road quality. Identification of maintenance severity level and providing repair solution is done using WEKA tool 3.7. This paper presents comparison summary of classification approach and estimated which algorithm gives efficient accuracy for classification. In this paper we have obtained highest accuracy of classification 98.84 % by Support Vector Machine (SMO Function).

关键词： Pothole Patches Road Cracks classification algorithm Accuracy

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Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Compositing algorithms, Spectral Indices, and Sensors (vol 9, pg 546, 2017)

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REMOTE SENSING 2017年第7期9卷

作者： Chance, Eric W. Cobourn, Kelly M. Thomas, Valerie A. Dawson, Blaine C. Flores, Alejandro N. Virginia Polytech Inst & State Univ Dept Forest Resources & Environm Conservat 310 West Campus Dr Blacksburg VA 24061 USA Boise State Univ Dept Geosci 1910 Univ Dr Boise ID 83725 USA

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world's diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho's Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region. © 2017 by the authors.

关键词： Agriculture classification algorithm Irrigation Snake River Plain Water use

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